Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

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Journal profile

The Journal of Remote Sensing, an Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.

Editorial board

The Journal of Remote Sensing’s editorial board is led by Yirong Wu (Aerospace Information Research Institute, Chinese Academy of Sciences) and is comprised of experts who have made significant and well recognized contributions to the field.

Special issues

Accepting submissions for 5 special issues! Visit our Special Issues page to learn more about these issues, which focus on Google Earth Engine, radiation transfer model intercomparison vegetation canopies, deep learning meets remote sensing, time series analysis, and China's contemporary satellites.

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The Two Faces of “Case-1” Water

Morel’s “Optical modeling of the upper ocean in relation to its biogenous matter content (Case I waters)” (J. Geophys. Res. - Oceans, Vol. 93, pp. 107,49-10,768, 1988) laid the groundwork to model the optical properties of natural waters based on the concentration of chlorophyll ([Chl], in mg/m3). As stated in the abstract, it aims “tentatively to interpret the optical behavior of oceanic case-I waters,” where “Chlorophyll-like pigment concentration is used as the index to quantify the algal materials,” because [Chl] is routinely measured in marine/oceanic surveys. Specifically, Morel developed “statistical relationships between this index and the depth of euphotic layer, the spectral values of the attenuation coefficient for downwelling irradiance, or the scattering coefficient,” and further, “a pigment-dependent optical model is developed.” Thus, such a system allows many aspects of oceanographic applications when [Chl] (“this index”) is provided. In part, this system established [Chl] at the core of traditional ocean color remote sensing. To implement this system, however, it is necessary to have a complete understanding of the definition and evolution of this Case-1/Case-2 system, especially the qualitative definition of Case-1/Case-2 vs. the practical separation of Case-1/Case-2 as well as the quantitative modeling of the optical properties of Case-1 waters.

Research Article

Direct Retrieval of NO2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

Satellite retrievals of columnar nitrogen dioxide (NO2) are essential for the characterization of nitrogen oxides (NOx) processes and impacts. The requirements of modeled a priori profiles present an outstanding bottleneck in operational satellite NO2 retrievals. In this work, we instead use neural network (NN) models trained from over 360,000 radiative transfer (RT) simulations to translate satellite radiances across 390-495 nm to total NO2 vertical column (NO2C). Despite the wide variability of the many input parameters in the RT simulations, only a small number of key variables were found essential to the accurate prediction of NO2C, including observing angles, surface reflectivity and altitude, and several key principal component scores of the radiances. In addition to the NO2C, the NN training and cross-validation experiments show that the wider retrieval window allows some information about the vertical distribution to be retrieved (e.g., extending the rightmost wavelength from 465 to 495 nm decreases the root-mean-square-error by 0.75%) under high-NO2C conditions. Applying to four months of TROPOMI data, the trained NN model shows strong ability to reproduce the NO2C observed by the ground-based Pandonia Global Network. The coefficient of determination (, 0.75) and normalized mean bias (NMB, -33%) are competitive with the level 2 operational TROPOMI product (, ) over clear () and polluted ( molecules/cm2) regions. The NN retrieval approach is ~12 times faster than predictions using high spatial resolution (~3 km) a priori profiles from chemical transport modeling, which is especially attractive to the handling of large volume satellite data.

Research Article

Landsat-Based Monitoring of Landscape Dynamics in Arctic Permafrost Region

Ice-rich permafrost thaws as a result of Arctic warming, and the land surface collapses to form characteristic thermokarst landscapes. Thermokarst landscapes can bring instability to the permafrost layer, affecting regional geomorphology, hydrology, and ecology and may further lead to permafrost degradation and greenhouse gas emissions. Field observations in permafrost regions are often limited, while satellite imagery provides a valuable record of land surface dynamics. Currently, continuous monitoring of regional-scale thermokarst landscape dynamics and disturbances remains a challenging task. In this study, we combined the Theil–Sen estimator with the LandTrendr algorithm to create a process flow for monitoring thermokarst landscape dynamics in Arctic permafrost region on the Google Earth Engine platform. A robust linear trend analysis of the Landsat Tasseled Cap index time series based on the Theil–Sen estimator and Mann–Kendall test showed the overall trends in greenness, wetness, and brightness in northern Alaska over the past 20 years. Six types of disturbances that occur in thermokarst landscape were demonstrated and highlighted, including long-term processes (thermokarst lake expansion, shoreline retreat, and river erosion) and short-term events (thermokarst lake drainage, wildfires, and abrupt vegetation change). These disturbances are widespread throughout the Arctic permafrost region and represent hotspots of abrupt permafrost thaw in a warming context, which would destabilize fragile thermokarst landscapes rich in soil organic carbon and affect the ecological carbon balance. The cases we present provide a basis for understanding and quantifying specific disturbance analyses that will facilitate the integration of thermokarst processes into climate models.

Research Article

Through-Foliage Tracking with Airborne Optical Sectioning

Detecting and tracking moving targets through foliage is difficult, and for many cases even impossible in regular aerial images and videos. We present an initial light-weight and drone-operated 1D camera array that supports parallel synthetic aperture aerial imaging. Our main finding is that color anomaly detection benefits significantly from image integration when compared to conventional raw images or video frames (on average 97% vs. 42% in precision in our field experiments). We demonstrate that these two contributions can lead to the detection and tracking of moving people through densely occluding forest.

Review Article

An Introduction to the Chinese High-Resolution Earth Observation System: Gaofen-1~7 Civilian Satellites

The Chinese High-resolution Earth Observation System (CHEOS) program has successfully launched 7 civilian satellites since 2010. These satellites are named by Gaofen (meaning high resolution in Chinese, hereafter noted as GF). To combine the advantages of high temporal and comparably high spatial resolution, diverse sensors are deployed to each satellite. GF-1 and GF-6 carry both high-resolution cameras (2 m resolution panchromatic and 8 m resolution multispectral camera), providing high spatial imaging for land use monitoring; GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter, mostly monitoring marine targets; GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera, dedicated to environmental remote sensing and climate research, such as aerosol, clouds, and greenhouse gas monitoring; and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying, facilitating the accumulation of basic geographic information. This study provides an overview of GF civilian series satellites, especially their missions, sensors, and applications.

Research Article

A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology

The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) (), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 (). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.